import unittest

from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


_SCREAMING_SNAKE_CASE 		=      """▁"""

_SCREAMING_SNAKE_CASE 		=      get_tests_dir("""fixtures/test_sentencepiece.model""")
@require_sentencepiece
class SCREAMING_SNAKE_CASE_		(      __lowerCAmelCase  ,     unittest.TestCase		):
   __lowerCAmelCase           =				BertGenerationTokenizer
   __lowerCAmelCase           =				False
   __lowerCAmelCase           =				True







   def 						lowerCamelCase_	(     self						:						Union[str, Any]				):





    """simple docstring"""


    super().setUp()

    UpperCamelCase			  =      BertGenerationTokenizer(lowerCamelCase_   ,   keep_accents=lowerCamelCase_				)
    tokenizer.save_pretrained(self.tmpdirname				)







   def 						lowerCamelCase_	(     self						:						Dict				):





    """simple docstring"""


    UpperCamelCase			  =      """<s>"""
    UpperCamelCase			  =      1

    self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCamelCase_				)   ,   lowerCamelCase_				)
    self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCamelCase_				)   ,   lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						List[Any]				):





    """simple docstring"""


    UpperCamelCase			  =      list(self.get_tokenizer().get_vocab().keys()				)

    self.assertEqual(vocab_keys[0]   ,   """<unk>"""				)
    self.assertEqual(vocab_keys[1]   ,   """<s>"""				)
    self.assertEqual(vocab_keys[-1]   ,   """<pad>"""				)
    self.assertEqual(len(lowerCamelCase_				)   ,   1002				)







   def 						lowerCamelCase_	(     self						:						Optional[int]				):





    """simple docstring"""


    self.assertEqual(self.get_tokenizer().vocab_size   ,   1000				)







   def 						lowerCamelCase_	(     self						:						int				):





    """simple docstring"""


    UpperCamelCase			  =      BertGenerationTokenizer(lowerCamelCase_   ,   keep_accents=lowerCamelCase_				)

    UpperCamelCase			  =      tokenizer.tokenize("""This is a test"""				)
    self.assertListEqual(lowerCamelCase_   ,   ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]				)

    self.assertListEqual(
        tokenizer.convert_tokens_to_ids(lowerCamelCase_				)   ,   [285, 46, 10, 170, 382]   ,   )

    UpperCamelCase			  =      tokenizer.tokenize("""I was born in 92000, and this is falsé."""				)
    self.assertListEqual(
        lowerCamelCase_   ,   [
            SPIECE_UNDERLINE + """I""",
            SPIECE_UNDERLINE + """was""",
            SPIECE_UNDERLINE + """b""",
            """or""",
            """n""",
            SPIECE_UNDERLINE + """in""",
            SPIECE_UNDERLINE + """""",
            """9""",
            """2""",
            """0""",
            """0""",
            """0""",
            """,""",
            SPIECE_UNDERLINE + """and""",
            SPIECE_UNDERLINE + """this""",
            SPIECE_UNDERLINE + """is""",
            SPIECE_UNDERLINE + """f""",
            """al""",
            """s""",
            """é""",
            """.""",
        ]   ,   )
    UpperCamelCase			  =      tokenizer.convert_tokens_to_ids(lowerCamelCase_				)
    self.assertListEqual(
        lowerCamelCase_   ,   [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]   ,   )

    UpperCamelCase			  =      tokenizer.convert_ids_to_tokens(lowerCamelCase_				)
    self.assertListEqual(
        lowerCamelCase_   ,   [
            SPIECE_UNDERLINE + """I""",
            SPIECE_UNDERLINE + """was""",
            SPIECE_UNDERLINE + """b""",
            """or""",
            """n""",
            SPIECE_UNDERLINE + """in""",
            SPIECE_UNDERLINE + """""",
            """<unk>""",
            """2""",
            """0""",
            """0""",
            """0""",
            """,""",
            SPIECE_UNDERLINE + """and""",
            SPIECE_UNDERLINE + """this""",
            SPIECE_UNDERLINE + """is""",
            SPIECE_UNDERLINE + """f""",
            """al""",
            """s""",
            """<unk>""",
            """.""",
        ]   ,   )







   @cached_property
   def 						lowerCamelCase_	(     self						:						Any				):





    """simple docstring"""


    return BertGenerationTokenizer.from_pretrained("""google/bert_for_seq_generation_L-24_bbc_encoder"""				)







   @slow
   def 						lowerCamelCase_	(     self						:						int				):





    """simple docstring"""


    UpperCamelCase			  =      """Hello World!"""
    UpperCamelCase			  =      [1_8536, 2260, 101]

    self.assertListEqual(lowerCamelCase_   ,   self.big_tokenizer.encode(lowerCamelCase_				)				)







   @slow
   def 						lowerCamelCase_	(     self						:						Any				):





    """simple docstring"""


    UpperCamelCase			  =      (
        """This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"""
        """ add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"""
    )
    UpperCamelCase			  =      [
        871,
        419,
        358,
        946,
        991,
        2521,
        452,
        358,
        1357,
        387,
        7751,
        3536,
        112,
        985,
        456,
        126,
        865,
        938,
        5400,
        5734,
        458,
        1368,
        467,
        786,
        2462,
        5246,
        1159,
        633,
        865,
        4519,
        457,
        582,
        852,
        2557,
        427,
        916,
        508,
        405,
        3_4324,
        497,
        391,
        408,
        1_1342,
        1244,
        385,
        100,
        938,
        985,
        456,
        574,
        362,
        1_2597,
        3200,
        3129,
        1172,
    ]

    self.assertListEqual(lowerCamelCase_   ,   self.big_tokenizer.encode(lowerCamelCase_				)				)







   @require_torch
   @slow
   def 						lowerCamelCase_	(     self						:						str				):





    """simple docstring"""


    import torch

    from transformers import BertGenerationConfig, BertGenerationEncoder

    # Build sequence
    UpperCamelCase			  =      list(self.big_tokenizer.get_vocab().keys()				)[:10]
    UpperCamelCase			  =      """ """.join(lowerCamelCase_				)
    UpperCamelCase			  =      self.big_tokenizer.encode_plus(lowerCamelCase_   ,   return_tensors="""pt"""   ,   return_token_type_ids=lowerCamelCase_				)
    UpperCamelCase			  =      self.big_tokenizer.batch_encode_plus(
        [sequence + """ """ + sequence]   ,   return_tensors="""pt"""   ,   return_token_type_ids=lowerCamelCase_				)

    UpperCamelCase			  =      BertGenerationConfig()
    UpperCamelCase			  =      BertGenerationEncoder(lowerCamelCase_				)

    assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

    with torch.no_grad():
     model(**lowerCamelCase_				)
     model(**lowerCamelCase_				)







   @slow
   def 						lowerCamelCase_	(     self						:						Tuple				):





    """simple docstring"""


    UpperCamelCase			  =      {"""input_ids""": [[3_9286, 458, 3_6335, 2001, 456, 1_3073, 1_3266, 455, 113, 7746, 1741, 1_1157, 391, 1_3073, 1_3266, 455, 113, 3967, 3_5412, 113, 4936, 109, 3870, 2377, 113, 3_0084, 4_5720, 458, 134, 1_7496, 112, 503, 1_1672, 113, 118, 112, 5665, 1_3347, 3_8687, 112, 1496, 3_1389, 112, 3268, 4_7264, 134, 962, 112, 1_6377, 8035, 2_3130, 430, 1_2169, 1_5518, 2_8592, 458, 146, 4_1697, 109, 391, 1_2169, 1_5518, 1_6689, 458, 146, 4_1358, 109, 452, 726, 4034, 111, 763, 3_5412, 5082, 388, 1903, 111, 9051, 391, 2870, 4_8918, 1900, 1123, 550, 998, 112, 9586, 1_5985, 455, 391, 410, 2_2955, 3_7636, 114], [448, 1_7496, 419, 3663, 385, 763, 113, 2_7533, 2870, 3283, 1_3043, 1639, 2_4713, 523, 656, 2_4013, 1_8550, 2521, 517, 2_7014, 2_1244, 420, 1212, 1465, 391, 927, 4833, 388, 578, 1_1786, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [484, 2169, 7687, 2_1932, 1_8146, 726, 363, 1_7032, 3391, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
    # fmt: on

    self.tokenizer_integration_test_util(
        expected_encoding=lowerCamelCase_   ,   model_name="""google/bert_for_seq_generation_L-24_bbc_encoder"""   ,   revision="""c817d1fd1be2ffa69431227a1fe320544943d4db"""   ,   )

def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )		->	list:







 '''simple docstring'''
 UpperCamelCase			  =      len(UpperCamelCase_       )
 UpperCamelCase			  =      [[0] * n for i in range(UpperCamelCase_       )]
 for i in range(UpperCamelCase_       ):
  UpperCamelCase			  =      y_points[i]

 for i in range(2							,					UpperCamelCase_       ):
  for j in range(UpperCamelCase_							,					UpperCamelCase_       ):
   UpperCamelCase			  =      (
       (xa - x_points[j - i + 1]) * q[j][i - 1]
       - (xa - x_points[j]) * q[j - 1][i - 1]
   ) / (x_points[j] - x_points[j - i + 1])

 return [q[n - 1][n - 1], q]


if __name__ == "__main__":
     import doctest

     doctest.testmod()
